Detection of the position of a moving object and treatment method

ABSTRACT

The invention relates to a method for determining the position of an object moving within a body, wherein the body is connected to markers, a movement signal is determined based on the measured movement of the markers, images are taken from the object using a camera or detector, wherein the camera or detector is moved with respect to the object, it is determined from which direction or range of angles or segment the most images corresponding to a predefined cycle of the movement signal are taken, and using at least some or all of the images of the segment containing the most images for a specified movement cycle, an image of the object is reconstructed.

BACKGROUND OF THE INVENTION

The present invention relates generally to the detection of the positionor state of a moving object, preferably the detection of the position ofan object moving within a body, such as for example the position of anorgan or a tumor within a patient. The invention relates especially toimage sequence matching for respiratory state detection, which can beused for extracranial radio surgery.

The invention relates also to the determination of the respiratory stateby matching a pair or series of x-ray images, which are for exampletaken during free-breathing, to a corresponding 4D volume scan.

To apply radiosurgical methods to tumors in the chest and abdomen, it isnecessary to take into account respiratory motion, which can move thetumor by more than 1 cm. It is known to use implanted fiducials to trackthe movement of the tumor.

It is also known to track the movement of tumors without implantedfiducials. Reference is made to K. Berlinger, “Fiducial-LessCompensation of Breathing Motion in Extracranial Radiosurgery”,Dissertation, Fakultät für Informatik, Technische Universität München;K. Berlinger, M. Roth, J. Fisseler, O. Sauer, A. Schweikard, L. Vences,“Volumetric Deformation Model for Motion Compensation in Radiotherapy”in Medical Image Computing and Computer-Assisted Intervention—MICCAI2004, Saint Malo, France, ISBN: 3-540-22977-9, pages 925-932, 2004 andA. Schweikard, H. Shiomi, J. Fisseler, M. Dötter, K. Berlinger, H. B.Gehl, J. Adler, “Fiducial-Less Respiration Tracking in Radiosurgery” inMedical Image Computing and Computer-Assisted Intervention—MICCAI 2004,Saint Malo, France, ISBN: 3-540-22977-9, pages 992-999, 2004.

U.S. Pat. No. 7,260,426 B2 discloses a method and an apparatus forlocating an internal target region during treatment without implantedfiducials. The teaching of U.S. Pat. No. 7,260,426 B2 with respect to aradiation treatment device, as illustrated in FIG. 1 of U.S. Pat. No.7,260,426 B2, and with respect to a real-time sensing system formonitoring external movement of a patient, is herewith included in thisapplication.

U.S. application Ser. No. 10/652,786 discloses an apparatus and a methodfor registering 2D radiographic images with images reconstructed from 3Dscan data.

It is known to place external markers, such as IR-reflectors orIR-emitters, on a patient. The markers can be tracked automatically withknown optical methods at a high speed to obtain a position signal, whichcan for example be a breathing signal or a pulsation signal, beingindicative of for example the respiratory state.

However, the markers alone cannot adequately reflect internaldisplacements caused for example by breathing motion, since a largeexternal motion may occur together with a very small internal motion,and vice versa.

SUMMARY OF THE INVENTION

It is an object of the invention to provide a method and an apparatusfor determining the position of a moving object, such as for example atumor, within a body, such as for example a patient. The movement of theobject within the body can e.g. be caused by respiratory motion.

This object is solved by the method and the apparatus as defined in theindependent claims. Preferred embodiments are defined in the dependentclaims.

A method and an apparatus for detecting the state of a moving body orobject, such as for the detection of the respiratory state and thecorresponding position of an object moving within the body, ispresented.

The method can involve the use of a first dataset, such as a pluralityor series of first images that each show an internal volume of the body,preferably including the internal object or target region. The pluralityor series of first images can for example be a sequence of computertomography (CT) images each including three-dimensional informationabout the body and/or the object. A series of 3D CT data sets or imagescovering a specific period, such as e.g. at least one breathing cycle,is hereinafter referred to as a 4D CT.

Each 3D CT can be segmented to obtain information about for example theposition and/or outline and/or surface of an object, such as tumor,within the body or patient. Using a series of segmented 3D CTs, themovement of the object in the first dataset can be determined.

The problem is that the object or tumor moves probably at a time laterthan that of acquiring the first dataset within the body in a (slightly)different way due to e.g. respiration or pulsation, since e.g. the shapeof the tumor has slightly changed, or since the patient's restingposition is slightly changed. For subsequent treatment e.g. byradiation, however, the current position of the object or tumor shouldbe determined without the need to make a 4D CT.

According to an aspect of the invention, digital tomosynthesis (DTS) isused to register the patient or to obtain the current positioninformation of the object or tumor moving within the body or patient,especially to determine the position of the object for a specific movingor respiratory state.

Digital tomosynthesis is a limited angle method of image reconstruction.A sample of protection images is used to reconstruct image plainsthrough the object of choice. The back projection of the projectionimages on the tomographic image plane yields an accumulated destinationimage. Objects not located close to the tomographic plane will beblurred in the image, but objects like a tumor, which are located in theisocenter of the machine, will be intensified.

In general, a digitally captured image is combined with the motion ofthe tube or detector as performed in conventional radiographictomography. Contrary to CT, where the source or detector makes acomplete 360 degree rotation about the object, to obtain a complete setof data from which images may be reconstructed, only a small rotationangle, such as for example 5 or 40 degrees, with a small number ofdiscrete exposures, such as for example 10, are used for digitaltomosynthesis. This incomplete set of data can be digitally processed toyield images similar to conventional tomography with a limited depth offield. However, because the image processing is digital, a series ofslices at different depths and with different thicknesses can bereconstructed from the save acquisition, thus saving both time andradiation exposure.

Since the body is moving during image acquisition, motion artefacts aregenerated. According to the present invention, these artefacts can beavoided.

The current state of respiration during image acquisition is recordedusing for example the above-mentioned IR markers attached to the surfaceor a part of the surface of the object or patient moving due to e.g.respiration.

Each periodic or almost periodic movement or motion, such as respirationor pulsation, is divided into sections, such as e.g. respiratory states,as shown in an embodiment in FIG. 2. The respiratory state can be forexample: inhaled, nearly inhaled, intermediate, nearly exhaled andexhaled. However, a coarser or finer division of the periodic signal orIR-respiratory curve can also be used.

Cone-beam computed tomography (CBCT) is a data acquisition method beingable to provide volumetric imaging, which allows for radiographic orfluoroscopic monitoring throughout a treatment process. Cone-beam CTacquires a series of projections or images over at least a part of orthe entire volume of interest in each projection. Using well-knownreconstruction methods, the 2D projections can be reconstructed into a3D volume analogous to a CT planning data set.

According to the present invention, cone-beam CT raw images are takenpreferably from different direction or angles, while the position of thecamera or sensor and the time of the respective image acquisition isrecorded and correlated to a movement signal, such as the IR-respiratorycurve. Thus, it is known for every acquired image to which movement orrespiratory state it belongs and from which direction it was taken.After recording several images together with this time and positioninformation, it is analyzed for every movement or breathing state fromwhich direction or angle or range of angles the most images have beentaken. In other words, it is determined for e.g. a pre-segmenteddivision of all possible acquisition angles, in which segment thelargest number of images has been taken.

Using this accumulation of images taken from different angles lyingwithin a predefined segment or within a predefined range of angles,digital tomosynthesis (DTS) is computed to obtain a DTS-image of theobject of interest.

It is possible to additionally consider images from the segment opposingthe segment with the most images for improving the generated DTS image.It will be understood that the data of the opposing segment has to bemirrored to be used as additional data for improving the DTS-image.

Additionally, at least one further image being preferably taken under adifferent angle, such as perpendicular to the calculated DTS-image, canbe taken into account for the same respiratory state. Thus, the 3D shapeor position of the object of interest or tumor can be calculated. Forexample, if the main direction of the motion of the object is the sameor close to the viewing direction of the reconstructed DTS image, it isquite difficult to obtain accurate registration results. However, if afurther image is taken into account which image is taken from adifferent viewing angle, such as plus or minus 90 degrees, registrationis quite simple.

Preferably tomographic images are computed for multiple or allrespiratory states.

Using the known or recorded camera parameters of every tomographicimage, such as the angle of bisector, and the segmentation data of thecorresponding respiratory state (e.g. from a prior 4D CT), hereinafterreferred to as “bin”, the shape of the target can be computed and can besuperimposed on the image.

Small deviations can be compensated for using an intensity-basedregistration to obtain an accurate position of a target in everytomographic image, thus yielding an updated trajectory. In other words,the current position of an object or tumor at a specific time orbreathing cycle can be calculated using e.g. an earlier taken segmented4D CT and several DTS images, which eliminates the need for a furtherCT. Thus, the trajectory of a tumor can be updated.

The invention relates further to a computer program, which, when loadedor running on a computer, performs at least one of the above describedmethod steps. Furthermore, the invention relates to a program storagemedium or computer program product comprising such a program.

An apparatus for determining the position of an object moving within abody comprises a tracking system, such as an IR tracking system, whichcan detect the position of external markers fixed to at least part ofthe surface of the moving body; and comprises a camera being e.g. anx-ray detector which can be moved with respect to the body, preferablypartly or fully around the body, the camera and the tracking systembeing connected to a computational unit correlating the marker signalsbeing movement signals obtained by the tracking system and the camerasignals including the image data and image parameters comprising atleast the time the image has been taken and the position of the cameraat the time the image was taken, the computational unit determining asegment or viewing range within or from which the most images were takenand elects this segment for image reconstruction, preferably by DTS.

According to a further aspect the invention relates to a treatmentmethod using the position or trajectory of the object to be treateddetermined by the above described method, for controlling and/or guidinga radiation source, especially controlling and guiding the position ofthe radiation source from which the body or object is irradiatedtogether with switching the radiation source on and off depending on thestate of the object or body, especially the position of the objectwithin the body, preferably considering the position of other objectswhich should probably not be irradiated.

According to a further aspect, the invention relates to the matching ofimage sequences, preferably for respiration state detection, which canbe used in extracranial radiosurgery. For extracranial radiosurgery themotion of a body, such as e.g. the respiratory motion, has to beconsidered, since this motion may cause a tumor to shift its position bymore than 1 cm. Without compensating this motion, it is unavoidable toenlarge the target volume by a safety margin, so that also healthytissue is effected by radiation and therefore lower doses must be usedto spare healthy tissue.

A method to compensate for this motion is gating which means that theirradiation beam is switched off each time the target moves out of apredefined window. The movement of the target or tumor can be determinedusing data of a sensor or camera, such as infrared tracking, to obtaininformation about the movement of the body, e.g. the respiratory curveof a patient.

A further method to compensate for this motion is chasing, where thesource of radiation is actively tracked or moved so that the irradiationbeam is always focussed on the object or target.

A method for determining the state of a moving body, such as therespiratory or pulsation state of a patient, which moves permanentlyand/or periodically, includes acquiring an image sequence, which can bean x-ray image sequence. This image sequence is compared to a priortaken sequence, such as a 4D CT scan, to determine the state of thebody. Thus, the position or trajectory of the object or tumor correlatedto the movement cycle or breathing state can be calculated.

The 4D CT scan can be segmented and/or otherwise analyzed, so that foreach scan or dataset of the 4D CT the state, such as the respiratorystate, is known.

If it can be determined to which prior taken scan or dataset the imagesequence corresponds, the moving state or respiratory statecorresponding to the respective image sequence or the respective imagesbeing part of the image sequence is known.

If just a single image or shot is taken and this single image should becompared to a previously taken sequence to determine the respiratorystate, the image found to best match one image or shot in the previoustaken image series is probably an image not having the same respiratorystate as the found “matching” image.

The reason is that single images taken during free-breathing do notdiffer that much and the comparison of a single image to images of aseries is quite complicated and does not necessarily provide the desiredresult.

If, however, the later taken image sequence(s) are compared as sequence(and not as individual pictures) with the previously taken imagesequence, which is possible if the previously taken and later takenimage sequence is taken with the same frequency, a whole sequence can betaken into account, thus eliminating the need to find a match for justone single shot in a series of previously taken images.

According to an embodiment of the invention, the frequency used fortaking the image sequence or image sequences is preferably the same orclose to the frequency of the previously taken images or datasets, suchas the previously taken 4D volume scan. Using the same frequencyprovides the advantage that the whole sequence of images can be takeninto account to compare this image sequence with the previous takensequence.

Considering for example breathing motion, there are basically twoindicators: the ribcage and the diaphragm.

It is obvious that the term “same frequency” should be understood toalso cover (integer) multiples of the imaging frequency of one imageseries. If for example the prior taken image series is taken with thefrequency 2*f₀ and the later taken image sequence is taken with thefrequency f₀, then the comparison can be made between the later takenimage series and the first taken image series while leaving out everysecond picture of the first taken image series.

In general, it is not essential that the frequency has to be the same,as long as the time or time differences between the respective images ofone image series is known, so that the respective single images of eachimage series can be compared to probably corresponding images of adifferent image series having basically the same or a similar timedifference in between.

If an image series of two-dimensional images is compared to a series of3D images, such as a 4D CT, then a reconstruction can be performed toobtain 2D images out of the 3D image series. A well-known method forobtaining radiographs out of a 3D CT-scan is to use digitalreconstructed radiographs (DRR), which DRRs can be compared to theprobably later taken image series.

It is noted that the later taken image series does not necessarily haveto be taken from the same point of view or angle, as long as thisimaging parameter, i.e. the direction from which the image is taken, isknown and recorded. Using this positional information of the camera orsensor, the corresponding DRR can be calculated from each 3D datavolume.

According to a further aspect, the invention provides a method fordetermining the way of treatment of an object within a moving body,preferably by radiation therapy or radiosurgery.

A dataset, such as a 4D CT, is provided, which is preferably segmentedand includes information about the region of interest which can includeinformation about a target volume and information about organs at riskwhich should not be affected by the treatment and should for example notbe irradiated by using radiation therapy as treatment method.

The position and/or orientation of the regions of interest are analysedin every bin which enables the system to provide guidance to the user.

A possible guidance can be a recommendation concerning the type oftreatment, i.e. whether or not gating and/or chasing is recommended.

A further recommendation can include an indication which bins should beused for the treatment. Based on the relative position and/ororientation of the planning target volume and one or more criticalregions or organs at risk, specific bins can be elected for treatments,whereas other bins can for example be sorted out, if an organ at risk iscloser to the planning target volume than a predefined safety distance,so that no therapy or irradiation is performed during that bin.

It is possible to combine two bins to a “treatment bin” if these two ormore bins do not differ regarding a specified criterion, e.g. thedistance between the planning target volume and an organ at risk.

It is possible to generate further synthetic bins using known techniquessuch as morphing or interpolation to generate e.g. a bin “intermediate”,if only data is available for the respiratory state “inhaled” and“exhaled”. If more bins are created; a more accurate 4D dosedistribution can be calculated and used for treatment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustration of a device used for radiotherapycontrolled according to the invention;

FIGS. 2A to 2C show a respiratory curve being divided into respiratorystates;

FIGS. 3A to 3C illustrate methods for DTS image reconstruction

FIG. 4 is a flowchart illustrating a method for determining therespiratory state;

FIGS. 5A to 5C illustrate a registration procedure performed accordingto an embodiment of the invention;

FIG. 6 shows the matching of a sequence to treatment bins;

FIGS. 7A to 7C show the fine adjustment using intensity-basedregistration;

FIG. 8 shows the fitting of a trajectory through sample point;

FIG. 9 shows the segmentation of the trajectory of FIG. 8 into treatmentbins;

FIGS. 10A and 10B illustrate the generation of treatment parameters;

FIG. 11 shows the contour-based detection of a planning target volume;and

FIGS. 12A to 12C illustrate the reconstruction of object data.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

As shown in FIG. 1, a patient is positioned on a treatment table. Anirradiation device, such as a linear accelerator, can be moved withrespect to the patient. An x-ray source being positioned on one side ofthe patient emits x-rays in the direction of an x-ray detectorpositioned on the opposing side to obtain 2D images of a region ofinterest of the patient. The x-ray source and the x-ray detector can beconnected to the beam source or linear accelerator or can be movableindependent thereof.

As shown in FIG. 1, external markers, such as reflecting spots, areconnected or sticked to the surface, such as the chest, of the patient.The reflections of the external markers can be detected by a trackingsystem, which generates as an output a respiratory curve as shown inFIG. 2.

FIG. 2A shows a respiratory curve generated from a sequence of imagesreferred to as sample points.

As shown in FIGS. 2B and 2C, the respiratory curve can be segmented intoseveral different states, being for example inhaled, nearly inhaled,intermediate 1, intermediate 2, nearly exhaled and exhaled.

By moving the x-ray detector shown in FIG. 1 relative to the patient, aseries of images is taken, wherein the position of the x-ray detectorand the time at which the respective image is taken is recorded. Usingthe information from the respiratory curve acquired simultaneously withthe image acquisition by the x-ray detector, a series of images takenfrom different positions or angles can be collected or stored for eachrespiratory state.

FIGS. 3A to 3C show as an exemplary embodiment the respiratory state“nearly inhaled”, where a series of images is taken under respectivedifferent angles at the same or at later or earlier respiratory states“nearly inhaled” of a different cycle during some full breathing cycles.The circle representing a 360 degree angle corresponding to the cameraposition as shown in FIG. 3A is divided into 8 segments. After the imageacquisition with the x-ray detector is finished, it is determined inwhich of the 8 segments the biggest accumulation of images being shownas small circles is.

FIG. 3B shows the determined segment found to include the largest numberof images being the segment from which the DTS is computed in the nextstep. The plane perpendicular to the bisector of the selected segment isthe plane of the tomographic image to be computed, as shown in FIG. 3C.

Thus, tomographic images can be computed for multiple respiratory statesby repeating the steps explained with reference to FIG. 3 for everysingle respiratory state. Using the known camera parameters of everytomographic image (angle of bisector) and the segmentation data of thecorresponding respiratory state (bin), the shape of the target can becomputed and can be superimposed on the image. Deviations can becompensated for using an intensity-based registration to obtain theaccurate position of the target in every tomographic image. Preferablyintensity-based registration includes only a rigid transformation.However, it is also possible to perform an elastic registration.

To ensure robust registration results, a second tomographic image,perpendicular to the existing one, can be taken into account for thesame respiratory state, as shown in FIG. 3C with the arrow DTS 2. Forexample, if the main direction of tumor motion is the same as theviewing direction of the reconstructed DTS image, it will be verydifficult to get accurate registration results. But if a further imagetaken from another viewing angle (e.g. +90 degrees) is taken intoaccount, this problem can be solved, so that 3D information is obtained.

FIG. 4 shows a registration procedure to match a sequence of 2D imagesto a previously recorded dataset, such as a 4D volume scan of a patient.

According to the shown embodiment, the 2D image sequence is acquiredwith the same frequency, so that the sequence can be matched to the 4Dvolume scan, as explained hereinafter with reference to FIG. 5.

If the time span of an average respiratory cycle of a specific patientis for example about five seconds and a 4D volume scan consists of 8bins, the images of the sequence should be taken every (5000ms/(8×2−1))=333 ms.

FIG. 5 shows the registration method for matching the 2D image sequenceSeq 1, Seq 2, Seq 3 to the 4D CT sequence Bin 1, Bin 2, Bin 3, Bin 4,Bin 3, Bin 2, . . . .

The bold line shown below the respective designation of the sequence orBin should symbolize the state of the diaphragm being a possibleindicator for the respiratory state.

As can be seen in FIGS. 5A and 5B, there is no match between therespective sequence and the bins. The sequence is shifted with respectto the bins until a match is reached, as shown in FIG. 5C.

The registration is preferably performed 2D to 2D, i.e. a pre-generatedDRRs shall be matched to n images of the sequence. The accumulatesimilarity measure values shall be optimised and the best match sortsthe images of the sequence to the respiratory states of the 4D volumescan.

Similarity measures are known from the above mentioned K. Berlinger,“Fiducial-Less Compensation of Breathing Motion in ExtracranialRadiosurgery”, Dissertation, Fakultät für Informatik, TechnischeUniversität München; which is included by reference. Examples areCorrelation Coefficients or Mutual Information.

When using stereo x-ray imaging, this procedure can be performed twice,i.e. for each camera, to further enhance the robustness by taking intoaccount both results.

Preferably, the two x-ray images of the pair of x-ray images areperpendicular to each other and are taken simultaneously. To perform the2D/4D registration, several independent 2D/3D registration processesusing e.g. DRRs can be performed. Both x-ray images are successivelymatched to all bins of the 4D CT and the best match yields therespiratory states.

As shown in FIG. 2A, the images of the sequence and their position intime of the corresponding respiratory curve is depicted. The respiratorycurve from IR is used to select one image per treatment bin (respiratorystate) and to sort the images by the respiratory state, as shown in FIG.2C. All points on the respiratory curve are sample points where an x-rayimage has been taken. The sample points marked with an “x” additionallyserve as control points for segmenting the trajectory computedafterwards.

The sequence is matched to the treatment bins, as shown in FIG. 6. Theimages of the sequence are moved synchronously over the treatment bins(DRRs) and the accumulated similarly measure is optimised.

The result sorts every single image to a bin and therefore to arespiratory state. The isocenters of the bins serve as control points ofthe trajectory, i.e. the isocenters were determined in the planningphase.

If no 4D CT is available (3D case), the planning target volume (PTV) canbe manually fitted to some well distributed single images. In the 3D and4D case, the contour of the PTV can be interpolated geometrically overall images of the sequence.

FIG. 7A shows an example, where the first and the last contour match isknown and between these images the interpolation is performed, yieldingan approximate match.

Fine adjustment using intensity-based registration can be performed forevery single image, so that no sequence matching is performed.

FIG. 7B shows that the intensity of the target is now taken intoaccount.

FIG. 7C shows the thereby reached perfect match. Finally, visualinspection can be performed by the user and if necessary manualcorrection can be performed.

So the position of the PTV in every single image can be determined,which can be used to define a trajectory in the next step.

For generating the parameters for treatment (4D), a trajectory is fittedthrough the sample points, as shown in FIG. 8, and the control pointsare used, wherein the trajectory is divided into (breathing phase)segments, as shown in FIG. 9.

Images located between two control points (marked as ‘x’ in FIGS. 8 and9), are sorted to a respiratory state or control point by matching theseto the two competing bins. The image is assigned to the best matchingcontrol point. After this sorting procedure is completed, the segmentscan be determined as visualized in FIG. 9. Each segment stands for aspecific respiratory state and therefore treatment bin.

To assist in the adding of trajectory segments to a chasing area (thechasing area is the area where the beam actually follows the target,outside this area the beam is switched off (gating)), the standarddeviation from sample points of a specific segment to the trajectorytaking into account the relative accumulation should be minimized. It isadvantageous to find the most “stable” or best reproducible trajectoryor trajectories to be used for later treatment by irradiation. Havingdetermined the best reproducible trajectories, the treatment time can beminimized since the beam can be quite exactly focussed while largelysparing out healthy tissue.

Regions neighboring critical bins (segments) are omitted

-   -   User control:        -   Visualization of DRR of specific bin with organs at risk            (OAR) and isodoses drawn in        -   Treatment time        -   Expected positioning deviation (how “reproducible” is a            trajectory)

For generating the parameters for treatment (3D) the following steps areperformed:

-   -   Fitting of trajectory through sample points    -   Definition of beam-on area in IR respiratory curve    -   Computation of trajectory segment (chasing area) based on sample        points located in the beam-on area (see FIG. 10)    -   Display of trajectory segments with high standard deviations    -   Display of expected treatment time    -   Display of the selected trajectory segment    -   Manual readjustment to optimize treatment time, standard        deviations and chasing area    -   Automatical determination of the isocenter (sort of reference        isocenter with respect to chasing trajectory)    -   If necessary, export to treatment planning system (TPS) for        plan-update

The treatment in the 3D and 4D case have as input:

-   -   Gained correlation of IR-signal and trajectory segment(s)    -   Isocenter

Procedure:

-   -   Positioning of the determined patient isocenter to the machine        isocenter    -   Continuously recording of IR-signal and transferring the signal        into position on the trajectory    -   Within the segment to treat: chasing; outside: gating    -   Use gating (beam off) if an error occurs in the above        computations, e.g.:        -   IR marker is not visible        -   Changed pattern of the marker geometry        -   No corresponding trajectory position to current signal in            correlation model    -   It is possible to take verification shots        -   Based on trajectory position drawing in of the planning            target volume (PTV) to enable a visual inspection and if            necessary an intervention    -   It is possible to continuously take images during treatment        (yields sequence with lower frequency)        -   To document treatment        -   To permanently check and update trajectory automatically        -   Export information to TPS for possible plan-update

Error handling, e.g. during treatment, can have as input:

-   -   old image sequence    -   new image sequence

Procedure:

-   -   A) Displaced respiratory curve/Unchanged trajectory        -   i. Registration of old and new sequence (Algorithm can be            close to that described with reference to FIGS. 2C and 6,            but instead of the DRR sequence the old sequence is used)        -   ii. Showing tumor positions of old sequence in new one        -   PTV matches to new images        -   Correlation between IR-Signal and trajectory will be updated    -   B) Changed trajectory        -   i. Registration of old and new sequence (see above)        -   ii. Automatic detection if an update is necessary: indicator            is a towards inhalation falling similarity measure value            (see e.g. K. Berlinger, “Fiducial-Less Compensation of            Breathing Motion in Extracranial Radiosurgery”,            Dissertation, Fakultät für Informatik, Technische            Universität München; section 2.3.3)        -   Automatic image fusion (image to image, not whole sequence            as described when generating the sample points of the            treatment trajectory) to get updated tumor positions and            therefore the updated trajectory.

Incremental Setup of Gating and/or Chasing (for example treatment on adifferent day)

-   -   A) First fraction: as described so far, the DRR sequence        generated from the treatment bins is used for the initial        sequence matching (as described when generating the sample        points of the treatment trajectory; FIGS. 2C and 6).    -   B) Later fractions: instead of the DRR sequence, the sequence of        the last fraction can be used for the initial registration        procedure.

For a plan-update the following can be done:

-   -   A) Recommended trajectory segment (chasing area) is different        from initially planned bin (when using 4D-CT a bin is equivalent        to a trajectory segment)        -   a. Selection of the recommended bin for treatment        -   b. Planning of new beam configuration taking into account            changed relative position and orientation of PTV and OARs to            each other    -   B) Update of the planned dose distribution        -   a. Detection of the actual PTV position in the control            images using intensity-based registration (as described when            generating the sample points of the treatment trajectory)        -   b. Computation of the dose distribution actually applied to            the target        -   c. Taking these results into account, update the beam            configuration in a way to reach the originally wanted dose            distribution

Image subtraction can be performed to enable a detection of the tumor inevery single verification shot. Thus, there is no need for usingimplanted markers anymore. An initially taken image sequence of therespiratory cycle forms the basis of this approach. The thereby gainedinformation is stored in an image mask. Applying this mask to any newverification shot yields an image which emphasizes the contour of thetumor. The moving object is separated from the background.

There are two ways to generate the mask

-   1. Compute a mean image of the sequence by averaging the pixel    values of the sequence. That means for every pixel of the    destination image:

${I_{Mask}( {x,y} )} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}{{Seq}_{i}( {x,y} )}}}$

-    The average image has to be subtracted from the verification shot    to obtain the image with emphasized target contour.-   2. Compute a maximum image of the sequence. That means for every    pixel of the destination image:

I _(Mask)(x, y)=MAX_(i=1) ^(n)(Seq_(i)(x, y))

-    In this case the verification shot has to be subtracted from the    maximum image to obtain the image with emphasized target contour.

For contour-based PTV detection, as shown in FIG. 11, the known contourof the target and an x-ray image containing the target is used as input.The procedure includes the steps:

-   -   Applying an edge detector to the X-ray image (e.g. Canny Edge)    -   Matching of the contour to the edge image    -   Optimize similarity measure value

Cone-Beam Raw-Data can be used for Sequence Generation having as inputraw images of Cone-Beam imaging with known camera position; and theinfrared signal. An image sequence with known respiratory states can beobtained: Images are not located in the same plane, but with the knowncamera parameters this sequence can be matched to a 4D CT, as describedwhen generating the sample points of the treatment trajectory.Furthermore, the Cone-Beam volume is received as output.

Cone-Beam of moving objects can have as input raw images of Cone-Beamimaging with known camera position; and expected position of PTV forevery raw image (e.g. based on 4D CT scan and IR signal during Cone Beamacquisition).

As output the reconstructed Cone Beam dataset can be obtained.

The advantage of this reconstruction method is to properly display anobject that was moving during the acquisition of the raw images.

During the acquisition of Cone Beam raw images the objects are projectedto the raw images. In FIG. 12A below the non-moving object (blackcircle) is at the same position C+D during the acquisition of two rawimages. It is projected to position C′ and D′ on the raw images. Anotherobject (hollow circle) moves during acquisition. It is a differentposition A and B during acquisition of the two raw images. It isprojected to position A′ and B′ in the raw images.

During a conventional reconstruction, a mathematical algorithm solvesthe inverse equation to calculate the original density of the voxels.For non-moving objects like the filled black circle in FIG. 12B, thereconstruction result is of sufficient quality. If the object movesduring acquisition of the raw images, the reconstruction quality isdegraded. The object at position C′ and D′ is properly reconstructed toposition C+D in the voxel set. Accordingly the Cone Beam data set willdisplay the black circle (F). The hollow circle at positions A′ and B′in the images is not properly reconstructed because position A and Bdiffer. The voxel set will show a distorted and blurred object E.

The new reconstruction algorithm shown in FIG. 12C takes the positionC+D during acquisition into account. It calculates the projectionparameters of the Object (hollow circle) to the raw images. Theseparameters depend on the object's position during acquisition of theimages. By doing this the beams through the object on the raw images (A′and B′) will intersect at the corresponding voxel in the Cone Beam dataset (A+B)). The object is reconstructed to the correct shape G. Insteadthe stationary object is now distorted to the shape H.

1. Method for determining the position of an object moving within abody, wherein the body is connected to markers, a movement signal isdetermined based on the measured movement of the markers, images aretaken from the object using a camera or detector, wherein the camera ordetector is moved with respect to the object, it is determined fromwhich direction or range of angles or segment the most imagescorresponding to a predefined cycle of the movement signal are taken,and using at least some or all of the images of the segment containingthe most images for a specified movement cycle, an image of the objectis reconstructed.
 2. Method according to claim 1, wherein thereconstructed image is a tomographic image.
 3. Method according to claim1, wherein the image reconstruction is done by digital tomosynthesis(DTS).
 4. Method according to claim 1, wherein the method is performedfor each segment of a movement cycle of the movement signal.
 5. Methodaccording to claim 1, wherein the reconstructed or tomographic image iscompared with a pre-segmented 4D CT dataset to obtain the outline orsurface of the object.
 6. Method according to the previous claim,wherein a trajectory of the moving object is calculated using thereconstructed or tomographic images.
 7. Method according to claim 1,wherein the movement signal is a breathing signal or a pulsation signal.8. Method according to the previous claim, wherein the breathing signalis divided at least into the following states: Inhaled, nearly inhaled,intermediate, nearly exhaled and exhaled.
 9. Method according to claim1, wherein the segment opposing the segment with the most images is usedfor reconstructing the image or topographic image.
 10. Method accordingto claim 1, wherein at least one image taken from a different angle orfrom a 90 degree angle with respect to the bisector of the selectedsegment is used to determine the position of the object.
 11. Methodaccording to claim 1, wherein the registration of the object is a rigidintensity based registration.
 12. Method according to claim 1, whereinthe detector or camera moves at least once around the object or along acircle or section of a circle.
 13. Computer program which, when loadedor running on a computer, performs the method of claim
 1. 14. Programstorage medium or computer program product comprising the program of theprevious claim.
 15. An apparatus for determining the position of anobject moving within a body comprising: a tracking system which candetect the position of external markers fixed to at least part of thesurface of the moving body; and a camera or detector which can be movedwith respect to the body, the camera and the tracking system beingconnected to a computational unit correlating the marker signalsobtained by the tracking system and the camera signals including theimage data and image parameters comprising at least the time the imagehas been taken and the position of the camera at the time the image wastaken, the computational unit determining a segment or viewing rangewithin or from which the most images were taken and elects this segmentfor image reconstruction.
 16. Method for determining the state of amoving body, wherein a dataset of the moving body including severalimages taken at different times is compared to a second dataset or imagesequence of the body to find the best correspondence between the firstdataset and the second dataset, wherein the first dataset is taken withthe same frequency as the second dataset or one of the first and secondfrequencies is a multiple of the other frequency.
 17. Method accordingto the claim 16, wherein the second dataset is shifted with respect tothe first dataset in time to determine a correlation or matching value.18. Method according to claim 16, wherein the first dataset is a 4Dcomputer tomography (CT) dataset.
 19. Method according to claim 16,wherein a digital reconstructed radiograph (DRR) is reconstructed fromeach three-dimensional dataset of the 4D CT.
 20. Method for determiningthe parameters of a treatment of an object moving within a body, whereina movement indication is provided and treatment bins are generated usingthe method for determining the position of an object according to claim1 and/or the method for determining the state of a moving body accordingto claim
 16. 21. Method according to claim 20, wherein a synthetictreatment bin is generated by morphing or interpolation of two bins. 22.Method according to claim 20, wherein the treatment is radiationtherapy.